{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial on the \"Model Plot\"\n",
    "\n",
    "The `hddm.plotting._plot_func_model` model, which we can supply to `hddm.plotting.plot_from_data` or `hddm.plotting.plot_posteriors` functions has become quite versatile (complex).\n",
    "\n",
    "This tutorial is meant to illustrate some of it's capabilities. \n",
    "We can generate didactically useful plots of a variety of generative models.\n",
    "The **LAN Tutorial** also shows some of the usecases.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install (colab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# package to help train networks\n",
    "# !pip install git+https://github.com/AlexanderFengler/LANfactory\n",
    "\n",
    "# package containing simulators for ssms\n",
    "# !pip install git+https://github.com/AlexanderFengler/ssm_simulators\n",
    "\n",
    "# packages related to hddm\n",
    "# !pip install cython\n",
    "# !pip install pymc==2.3.8\n",
    "# !pip install git+https://github.com/hddm-devs/kabuki\n",
    "# !pip install git+https://github.com/hddm-devs/hddm"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODULE IMPORTS ----\n",
    "\n",
    "# warning settings\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
    "\n",
    "# Data management\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "\n",
    "# Stats functionality\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "\n",
    "# HDDM\n",
    "import hddm\n",
    "from hddm.simulators.hddm_dataset_generators import simulator_h_c"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note** a slight difference in how we treat generative models. \n",
    "Thanks to the LAN-extension, we have many generative models available to us, which we can broadbly classify as:\n",
    "\n",
    "1. Legacy models, now titled: `ddm_hddm_base` and `full_ddm_hddm_base`. The `full_ddm_hddm_base` model is used when any of the `sv`, `st` or `sz` parameters are positive.\n",
    "2. 2-choice models enabled by the LAN-extension: amongst others the `levy`, `weibull` and `angle` models.\n",
    "3. n-choice models enables by the LAN-extension: amongst others the `race_3` and `race_4` models.\n",
    "\n",
    "The **model plots** work best with the 2-choice models enabled by the LAN-extension. \n",
    "A n-choice version for the **model plots** exists (see the `_plot_func_model_n` function) but is less well tested.\n",
    "**Model plots** also work for the `hddm_base` models, however the positioning of the *reaction time and choice* histograms is a little more tricky, and will sometimes lead to unsatisfactory results.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Simple Data from `ddm_hddm_base` model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hddm.simulators.hddm_dataset_generators import simulator_h_c\n",
    "\n",
    "model = \"ddm_hddm_base\"\n",
    "n_samples = 1000\n",
    "\n",
    "data, full_parameter_dict = simulator_h_c(\n",
    "    n_subjects=1,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions=None,\n",
    "    depends_on=None,\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.279359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.632366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.041361</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.648366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.437368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.478368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.711365</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>3.343362</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.670366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.893363</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.241863</td>\n",
       "      <td>2.258145</td>\n",
       "      <td>0.605863</td>\n",
       "      <td>2.262367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response subj_idx         v         a         z         t\n",
       "0    3.279359       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "1    2.632366       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "2    3.041361       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "3    2.648366       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "4    2.437368       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "..        ...       ...      ...       ...       ...       ...       ...\n",
       "995  2.478368       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "996  2.711365       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "997  3.343362       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "998  2.670366       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "999  2.893363       1.0        0  1.241863  2.258145  0.605863  2.262367\n",
       "\n",
       "[1000 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hddm.plotting.plot_from_data(\n",
    "    df=data,\n",
    "    generative_model=model,\n",
    "    save=False,\n",
    "    make_transparent=False,\n",
    "    path=\"tmp_figures\",\n",
    "    value_range=np.arange(-0.1, 5, 0.1),\n",
    "    plot_func=hddm.plotting._plot_func_model,\n",
    "    keep_frame=True,\n",
    "    **{\"hist_bottom\": 0.0}\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Simple Data from a model enabled by LAN-extension\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hddm.simulators.hddm_dataset_generators import simulator_h_c\n",
    "\n",
    "model = \"angle\"\n",
    "n_samples = 1000\n",
    "\n",
    "data, full_parameter_dict = simulator_h_c(\n",
    "    n_subjects=1,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions=None,\n",
    "    depends_on=None,\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>v</th>\n",
       "      <th>a</th>\n",
       "      <th>z</th>\n",
       "      <th>t</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.117624</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.047633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.491619</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.583618</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.662617</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1.283622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.259643</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1.866625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1.129624</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.129637</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.832015</td>\n",
       "      <td>1.953756</td>\n",
       "      <td>0.426032</td>\n",
       "      <td>0.685626</td>\n",
       "      <td>0.745569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response subj_idx         v         a         z         t  \\\n",
       "0    1.117624       1.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "1    2.047633       0.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "2    1.491619       1.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "3    1.583618       1.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "4    1.662617       1.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "..        ...       ...      ...       ...       ...       ...       ...   \n",
       "995  1.283622       0.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "996  2.259643       1.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "997  1.866625       0.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "998  1.129624       1.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "999  2.129637       1.0        0  0.832015  1.953756  0.426032  0.685626   \n",
       "\n",
       "        theta  \n",
       "0    0.745569  \n",
       "1    0.745569  \n",
       "2    0.745569  \n",
       "3    0.745569  \n",
       "4    0.745569  \n",
       "..        ...  \n",
       "995  0.745569  \n",
       "996  0.745569  \n",
       "997  0.745569  \n",
       "998  0.745569  \n",
       "999  0.745569  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hddm.plotting.plot_from_data(\n",
    "    df=data,\n",
    "    generative_model=model,\n",
    "    save=False,\n",
    "    make_transparent=False,\n",
    "    path=\"tmp_figures\",\n",
    "    value_range=np.arange(-0.1, 5, 0.1),\n",
    "    plot_func=hddm.plotting._plot_func_model,\n",
    "    keep_frame=False,\n",
    "    **{\"hist_bottom\": 0}\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can move around the histograms with the `hist_bottom`, argument (`kwarg`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hddm.plotting.plot_from_data(\n",
    "    df=data,\n",
    "    generative_model=model,\n",
    "    save=False,\n",
    "    make_transparent=False,\n",
    "    path=\"tmp_figures\",\n",
    "    value_range=np.arange(-0.1, 5, 0.1),\n",
    "    plot_func=hddm.plotting._plot_func_model,\n",
    "    keep_frame=False,\n",
    "    **{\"hist_bottom\": data.a.values[0], \"ylim\": 3}\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at a few other arguments to illustrate the range of styling options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvvqqNz799NPTusnVZ+XKlQwdOpRYLAao9cKzz17FjBlBJk+Ge+75zqcsCEIWkDUiOHfuXI466ihv/MEHHzBu3Lgm75kyZQp33XWXNy4u3sGuXe3p0wdWrwZpTSIIQk66wy433XSTr6/uSHbtUqEya9fC55/vv3kJgtB6aFHLzWxhxQpX5Ao488yXePjhh4DTgQRgYhhw331w2mnp940YAYMHf+/TFQRhP5KTInjzzTB7tjsam3wBKM/fshyef17j+efT7zv7bHj55e9pkoIgtApy0h1++mk455xMZ7R67ynOPReeeuq7nJUgCK2RnBTBdu3gxRdh1iwIh8FsxN41TXX+mWfghRfUfYIgtC1yUgRB7fz++teweDEEApmvMU2bxYvhwgtlp1gQ2io5K4IuhgF1dZnPRSJ6o1aiIAhtg5wXwdmzQU/+SsNwQyLjyXeLe+/9dn9MSxCEVkLOi+BLL4FbW/XoozWeffYL4APUTrHOc89FpEOdILRhcloE161TecKGodLk/vtfuOCC4Vx44fPAjYBFJHIQ06Y9v7tHCYKQo+S0COo6nHwyfPghTJ6ccounTbuL4uIngCOB17jvvnvZunXr/pyqIAj7iazJHd6wYQMvvfSSNz7nnHPo0aPHHj9vxowZXH311d740ksv5Y9//ONezVEQhOwja0RwXxOPxxk5ciRffvkloH7fp59+yujRo/fzzARB+D7JaXe4KQKBQFqHOsdxmDhxotehToqvCkLboM2KIMBxxx3H2Wef7Y3nz5/PX//6Vym+KghtiDYtggD3338/eXl53njy5Mk8+2yUeBz+9rf9ODFBEL4X2oQI7twJ27ZlPte7d29uuukmb7x582aeeUa17HzpJWjeiqkgCNlK1ojg3Llz0TTNe82dO7fZ986YAb7lvwZcd9119O/fPzkaSVVVCSDFVwWhLdCmM2dTxVfDnHnmS9x3372kF191uO8+jeOPB5/HLMVXBSGHyHkRjERSn207FTAN9YuvjgHcOES3+Co8/zxSfFUQcpiscYf3lPnzU5+nTlXZIy5SfFUQhJwXwR07Up8dB4LB1Li5xVddZs2S4quCkGvkvAhWVakCCi6hUPr55hRfdRk1SoqvCkKukfNrglVVMHo0fPKJGofDma9bs6bx4quAdKgThBylTYhgu3bQqxesX9/QEnS57rqmnyMd6gQhN8lpdziRgGgUCgpSbqwrgm++qUTRRd/tX0I2SQQhF8lpEaxViR8ZRfDDD9UmB6jiq0uWKCE8+2zpUCcIbYk2IYL5+Q1F0I9bfHX+fOXaNt2hzpEOdYKQQ+T0mqBfBMeOVRZfKJTKB3ZFrGdPeO211H1Nd6jTpEOdIOQQWfPPOS8vj4EDB6aNd0d1tXovKICSErj1VjW2rKbvczvU2bbqUGdZGqpDXQCweOCB9Tz6aN89+RmCILQyssYdHj16NF999ZX3ak4F6C++gC5dGrq2uxPB+h3qZs36HH+HumeeqcXa3UMEQcgKskYE94QdO6B//4Zrd26HzUxrev4OddOmKevx5JNHcP75z+F2qKurO4jp01/8rqcvCML3QE6LYDyeeYOjKSPO36Hu2mvh3Xdh82aYPn0aRUWP43aomz79HrZv3/4dzVwQhO8LEcF69OwJr7wChx2WshgNA7p168att94KfAKcys6dS7nlllu+i2kLgvA9ktMimEhkFsGm3OFEQrnBS5akxNLNPZ40aRKDBg3yrn3iiSdYvHjxPp61IAjfJ1kjgitXruQ3v/mN91q5cmWT1zuOsgQzhbM0ZQlu3ao2RUpKUte5zwgGgzz88MPetbZtM3HiRNyupdKhThCyj6wRwU2bNvGnP/3Je23atKnJ6y1LCWFL3eEtW9R7p04NLUGA8ePH89Of/tQbz507lxdeeEE61AlClpI1IthS4nH13hx3+KuvoLJSfd65UwVXh8Ppa4J+HnjgAcK+cjTXX389zz0Xkw51gpCF5KwIugLWlDtcVQXPPacKq7qFEOLxVOHVTJYgQN++fbnhhhu8cXl5ObNm1QDSoU4Qso2cFcGmLEG/O7x6tXqvqlKpcrFYQxHMJKQ33HADffr0SY6kQ50gZCtZkzbXUprjDtfn66/VfV9+CY8+Cqeeqo77LcFUh7o8zjrrJR544AEydaiT4quCkB3krAi6QteSjZF4XFmCZWXQsWNmdzi9Q90PkA51gpDd5Kw77FaB8TdWconFUp/9sYKxmBJCV/QyiaB0qBOE3CJnRXDLFmUFZip6unNn6nO3bqnPsZh6uaIXjap3/5pgczvUucVXH3tMiq8KQmsmZ0Vw40ZVQSZT2Xy3DWdpKRx4oPpcVKRKb33zTeqeXbvUe/3d4eZ0qDNNmxdegCuvVNcJgtA6yUkRjMdV7J+v/CCgrMNYTFmCI0bAxIlwzDFwxRVQWKg60rkVZKBxEXRpuviqzhtvILGDgtDKyUkR3LpVic+AAenHH3tMbWr4w2B0XW2CBIPquGWlRK9Ghf41KoJu8VV1jRscmNyWxuKFF9RniR0UhNZLToqg6+6WlKSOuSK0aZMSuoqKlMiBEkHbbiiChtF4LxF/8dXDD9d49tnlwFzc4qs1NcpXlthBQWi9ZE2IzPDhw3nnnXfSxplwHOX2hkLgr8DvT5WzLHj7bWUxXnutOhYMquP+yjOuCGbCX3z1ssuUuFZWDuG44+by9tsbgV/g7hRL43ZBaL1kjQi2a9eOY489drfXzZ2rCqF26tQw/AWU+xqLqXPV1arD3NixSgSjUSWi7o5vVVXju79u8dU//AGmToV//cuNH7zEd5UDaNK4XRBaMTnnDq9apd7r7wq7IqhpEImkzq9bp97D4dRGiGsJ1taq45lwO9Qddpiy8oYOzXSVxA4KQmsn50TQFa36GxF+S7CuLmUlum5yXl5qfc8f9hKJ7P478/LgrLPgoYfc2MHMuyDSuF0QWh9tTgTrW4KuCIbDKRH0u8DN2dWNx9Vzf/UrN3Yw805KICCN2wWhtZE1a4LV1dV8/fXX3njgwIEUFhY2uC4UUu9NWYLRaENLMBxOpcmFQlBcrNzj3XXW3LBBWXWgxLOp2MG6OmncLgitjayxBBcvXszo0aO9V2O9Pdz4v44d04+7VWU0TYmgawm6xx1HWYIHHqiuOfJIddy1DhvjzTdTQhkIpMcOalrD2MG//rV2t79VEITvj6wRwebiipavAj6QsgQdR1l/riW4ebNyjw1D3euGxOTnq/fdWYL+WMRAQBVpdecwYIDGxInP4m/c/uijW73rpSeJIOx/ck4ELQs6d065xS7uBkcioV7+3eOyMjjoIPXq3Fkdc0WwkXBED38sYlkZLF+uBHbCBJWOd++9v+CAA36L27i9oqI3c+askJ4kgtBKyEkRzBTg7FaEiUbVNa4laJoqaFrXoW/flDjm58NNN8FPftL09/nLcgEccABcdBH06qWeHQqFePjhGcB03Mbtt956Cy+84EhesSC0AtqMCLqWYDSabgmWlioRhHRBy89X64uZqtC4OI6qVuPSpQucdx706KHG7jxOOukkTjvtNNzG7R999L8884xaG5S8YkHYv+SkCGYSLlcEY7F0EczLS1mJ7iYJQEHB7r9r6VIoL0+N/fdDuhg/+OCDhDwffSTbtqkvkLxiQdi/5FzARiZLcP36VGYIKBEMBlWpLdtOhcn4LcHmhLL4CzB07dqwd4lp+nuSDODkk5/llVdeIb0nieQVC8L+pE2I4NNPKyEaMSJ1zcEHwy9/qdxR14KLxVQecd++zfsu/6bIZZepyjR+DKN+T5Jzky9I9SSRvGJB2J+0CRF0jy9cqM7V1KQyS0xTBTc7jhLD0tKGxVgbw7X8jj5avX/ySfp5w1ACbJpKbNNpOq/4iSeaNwdBEPaOnFwTzCSCbic5t7+IK4KBQCpsxnEaL5efCbc463HHqfGnn6p3t5iCYWTqSSJ5xYLQmsh5EXR3XuPx9NhBd+PDNNU51yXO1J2uMeLxzKLpusnuPNJ7kkhesSC0JnJeBCORVAZHYyKYSKQ2RVpiCfpF0L8zXF8EXSSvWBBaH1nzz65jx46ce+65aeNM1BdBNySmZ8/03Vu39oLrDrsi2CxLcO1aePtt4l0vJBhUZlutLyW4MRF084pVep6DZWmovOIAYDFrVhV33tm+GRMQBGFfkTUiOHjwYF588cXdXldfBBMJZaWZZnrYixsn6LrDrkA2yxKcOxfWrkV3NhAI9ATSw2VcEazv1vp7khx9tMZRR/2DO+4oAn4I6Dz++HYRQUH4nsk5dziRSBe7REIFQwcC6VWi3bVC1x12RbBZLmnSCjUryj3RdKtSjxmTsib9z/L3JLnnHvjvf+EPfziNQw+9CTeveMeO/rz88kct/cmCIOwFOSeC/naaoCzDujp1zC2KAKnP/t1haKYIJlNMEtURvvpKecdbt6o1x1NOSV1WXJz67PYk+fBDmDxZjXVd5/bb/4A/r3jSpIkk6kddC4LwnZHzIphIKBHs2DHlJnfunAqINk3lorprgo11l0sjubuxbmWUdetUaExFRaq5k1siyy+C/p4kHnffzapbb00OVF7xpk0LeEKCBAXheyOnRNBxMotgJKKCoAMBJVS+/RXP8nNzi5tlCUYixGJQuTlK+/ZK+GproahInR42DPr3333Q9ZeLFrF1wYIGx2+55RYq6qefCILwnZA1Irh48WIOPfRQ75WpsrQb8FxfBC1LCZSuq0DmAw9MnXfX9FokgnV1xONg1UYJBpVh6G6+gNp5/tWvMnSqW7/ea4e3bds2Xn/9de+U7qv6sHPnTqZMmdKMiQiCsLdkjQhWV1ezYMEC71VdXd3gmkxhLh98oETQL0j+867721JL0LZBj0fRdXVvY4HTaTz9NPzlL2zfDldeeR3Vvi3l66+/nmHDhnnjp556igUZrERBEPYtWSOCzSFT1sf69coa9Bc78JfJ8rvDhtF0tkYiAf/8J2zbqESwc81qxn3+KLHqWINd6aaYOPEbnn8+VbJm8ODB3HbbbcycOdM75jgOV1xxBfbumpwIgrBX5JQI1rcEXf2wrPSdYX+TOle4otHdb4rs2AELFzgsXxQlYesYVoyiSAX2zl3NswSBSCTCP//5T0D55Bowa9YswuEwxx57LD//+c+9az/++GP+/Oc/7/6hgiDsMTktgq5lmEiku8ONWYK7s+RsGwwrRiLusHBlMbathC9WZ5FINE8E58z5L5GqHd748MMP54gjjvDGl1zyEKHQcd74hhtuoLKycvcPFgRhj8gZEdywAaqrlZgtXKiOuRsllpWeNxwwbK/4X0tF0LRUjGCdWYzjJMU1Hqe2dvf3r169moWfLSKfODo2Je3bc9xxx6VdM3duV8aO/YM33rJlC7fffvtuf78gCHtGToig48CTT6qSVR99pLLa3PqArkscDMKZZ8KgQcDbb8Ojj0Is1nIRTKgdlEioHbatRFC3E7u1BGtqanj11VcBg0JimFRw+umnE8hw09ixY+nfv783njlzJl9++WUL/iKCIDSXnBBBN8Fi7Vr1bhhK1N59N3UuFFLVpH/xC1LdkRIJT/jKy9WaX1PYNhgJZQlGgioSOhwG3VJ+d1MiOmXKFLbv3AnoFBDjvJ8V069fv4zXmqbJjBkzfL8vwaRJk3CkI5Mg7HNyQgT9jdVVOhp88YWq3xePqwBp/+6wlzjsE0H/4cbwu8PbE8VomhJX3VZK25glOG/ePB5++GHUVTo/DZUx+erLUxdk6PB+6qmnctJJJ3njt956i7///e+ANG0XhH1JTomgpqV2eN3SVokE9OuXvjvcmAjubmPDtlOCt2x1vvd9ht24JRiJRLjoootwHAc1NYNDD+xFh5DvT++Wu/ahaRozZsxIc5evueYaPvywTpq2C8I+JOdE0BUit5dwPK4swzRL0F0oTCTSwmKuv77p77Ft0GxlteUXGWgadOjQtCV4++2389VXX6Gj/tgHDRlGaWkpgYSvuqq/ebGPgQMHcu2113rjdevWcd11C6VpuyDsQ3JKBCFVudld39uxQ4lgWgqbzxL0B0fvrqCq3xJ0QmHy85WF6a4JplmbwMKFC7n33nsBVTa1sKCAI4/6oRrX+Ky/bdvc6TRgypQp9HC7ufueW7MAACAASURBVAPz5/cGpGm7IOwrckoE/YLmNlTfuFG5rGkN2X2WoEtz+nrYNuiORceOsDm/H1/0Ox0tFPSE0V81JhaLMWHCBKzkel8RcMoppxAf92MAAtU78EzJ5GTrN28HKCws9IQURgJKBKVpuyDsG7KmsvThhx+eFjSc7zO7/CLour1+61CvL/U+SxBUxZdRo3Y/B9cdNoIGdkxja+9DIPSOtyboz0SZNm0aS5Ys8cbn//jHDBkyhBXtugJg7NqBZ0omRdC/P5Jq2g6adi6DB3/FihUH4jZt13Wb++7TpWm7IOwlWSOCpmlS7De1fLiC96tfwVtvpY4ddJByj884o94NriWYNL1+9avmzcG1BPWAgR1Jur+BAPkB9RzXmly6dCn/8z//491XWlrKrZdfDl98QaygBACjcrsqbRMKedUb/O7wlCmQ3AxGJdfdlvzsJOeiGrZL03ZB2DuyRgSbIhZTLm8olLL63GOrVsHmzfVuqGcJNhd3TdAIGjhOsn6gaXLqjxMcebj7yAQTJkwg7vNtH374YUoCASgsJO6YaBpoWytSJptbqdo3nT/9SW20SNN2QfhuyZk1wUAg3Z20bSV+W7fCvHn1bsiwJtgcXHc44RjYtlrOIxAgP5jA3bt48MEH+dTtwg6cdtpp/OIXv1BCFw4TJ6CE2rahRw+1Y5NBBAsL05u2N1bcwTAcadouCHtBTohgPK52duvHHL/3nnr3F0wAUhfuiSXoWPTsazJokCqZ73VqAr7++mtu9crlQ3FxMY8//jiapimhC4VIaIHUGmXPno26w/zlL2hLPveatjde4SYmTdsFYS/IGnd406ZN/Otf//LGp556Kl27qk0G1xJsrPReA3Fwt45bKIKrV0PFJouC0Qb9OybjApMiaNs2F110ERG3OivwwAMPpMJbkiIYd0zCOspv794d1qxpKIKOo7Z/+yQ3UYz0jR4/lhVi8eIFDBo0pkW/RRAERdaI4MqVK7nkkku88QcffOCJoFvLb/Xq9HtcC9DfGB2AaJTly6F4SJweP2j+HN54A0rKLTTTIJFIWmdJEXzssceYO3eud+3xxx/PhAkTUjdHIlBURMIx0AxddXsKBNTuSl0dOG4zdlWkwbFSHeFnz1ZC7qYFKrFPNW2/+uq5nH32KIxmdYkSBMFPzrjDkUiqgIKL24jdNfzcg048webNMHeOL2ujogJuu63JKgqOAyZK/SwrmZ1imlSUl3PjjTd61xUUFPDkk08qNxhUMHRZGVXRoLL2TDPpS6OU2rIgGk0Ve4hVKaFLiuDf/pbay+ndWxXA6dp1JWqnWKe8/Cj+9Kc/teyPJggCkCMiGItlXg+zbbVWGI/7sitiMc9tNuM+EVy6VL1v2dLo9zgOGCj1cwXWMQyee+opanz9QqZNm0Zft6cnwMcfE4vBnL9t4+OPoWLgkXDIIepcQQE7d8JXn9V4IhiMVqn5xmKsW6fWBDUNjj9ehfMMGwYLF5YQDP4BsIDR/P73j7EtmXkiCELzyQkRdDdGXCorU13m3OOeNZi0uBxNx/CL4Pbtu/0eTwSNlDs85513+Hr5cu+acePG8bvf/S79xg4dsG3YXjIAgC1DjlHrgQD5+SxeDKumvtDQEozH0XUYPx4uugiOPFKJYTAI3bt35e672+M2ba+s3MHNN9/c7L+ZIAiKnBFBt3CC4yjLacUKZQm6sYNuTjHRqHKfQ+2gtk4Jz9atqvZW8nxjuCLo6Mod3rGjgidmzfIWVsPhME8//XRa+0xAueDBEGW9VBn9sjLfuWSaSX7dtpQlGKv2LMGePVXoiy992KuSPXHiRIYMqQJOBTbwxz/+kUVSXkYQWkROiGAslgqSdi00d5M2GFTWk7dpm7QEI+H2BOK1LFgAPPJI6mG7E0EngaMbxOMO06ffza7aWk8E77jjDgZm6rieSOAEgmnP8cjLoy6vA3V5HdLcYf+aYP2dYVcEA4EADz/8sO+5DhMnTpTiq4LQArJeBCsqVJEEd02wpiZZ7Tn5y4LBepZgJEI8DnV5HTDjdQ3zipthCSYcg88/X8q8ee8mM3nh0EMP5eqrr858YyKBrTe+EV/RcQgOmhe+GIpVqc/JrJP6hRX8/VKOP/54zjzzTG88b948nq+fSycIQqNkvQi6ewEHHKDeFy9OiaCmpT6vXataizi1dcRiUJdfih6pZee2ehHWzRDBLduqef3114EYCSCk68yaNQuzsfr68XiTImjrJrqdYMcOlYrXXEvQ5f777yfsqxU2efJkqqT8tCA0i6wXQddK8tdW8OcQm6ZKJfvg7Tif/3MdtdvqiNoBzA7FlJdZvPpX1TPY7tAxraJLJhwHdGye++vLRCJ1uCJ4wrHHMmzYsMYnmUhgm42XrXZFcONG6NNHrQkmMJotgn379k0L0SkvL2fq1KmNz0cQBI+sF0E3PMZ1hw87TImEP7g4GoUBq95k1KJZfPp2FXVOmFBJProO/TbNZ948+E/gVOjYcbciWF25nY8XLnW/nX4HHMDRRx7Z9CQTCZykJdixowp18eOK4M6d0LmTQyhWRaywVP04x/FE8NBD1XtalewkkydPTgvLmTFjBitWrGh6XoIg5IYI+osndOiQyiN2S+v//Pjt5EVUEPTWr7axI5JHqET1COlX8QkAy1aGlNmYoemRSzQaZfPG9VgoFdJ1i1tuuw0zlcaRGZ87fM45MG5c+mlXBGMxCDkRAlqCaFFHpbqxGPG42uw56SS4+OL0uoUueXl5PPjgg76vjHPllVfKJokg7IacEMFgMLUr7DjpIhi2ahj0n4fpsH0lAHl129lWE6Zd1zw0zVdLwUyWamkin3jBgs9wrCg2av3tiisu5aDhw9XJpkQwkcA2lDvcYCOGpAg6FvGYQzBWja5DpLCjOhmJeEKv66lEk0ycccYZjB8/3hu/+eab/OMf/2j8BkEQsid3uG/fvkyfPj1tDCkRtCxVi6CqCoYMUVoWiUA4XoWmqcrRFRWwbsM2tpceyNGnFPG/v/dV1TLDaRVhQJXgWr0azj8fXn/9Ddas2YWOhUUepaUdufnmi2HLKnWxl0eXgUQCW1fCmSm917USS3Z8S6g6TtRIxjECRKPeb9wdmqbx0EMPMXz4cBLJ33H11Vdz4oknkpfJhxYEIXsswZ49e3L99dd7r55Jk8gtnpBIqB3gLVuUMHburASnNFSNpqnNkcJCVQor0C4PM6izsPtpWBbU5nfMaAnOmQMrV8KuXbu49NIrABODOBYFnHHGGRQX+wr9NeFGE49jaUroGrMEAUYs+Qulc/6mLMFAkTrZAhEEGDx4MFdddZU3XrNmja9HiSAI9ckaEcyEZcEnnyhrLpFID5gGaN8eioxUTq97PlCs+pMkgnnYNuxo3w80jVVrjIxidv31N7BhwzbAwMBi5CHj6NWrlyql1RwR9MUJZhJBR0+Zh7quhP2T5UUqqDqZ4dJcEQS45ZZbvAo7AHfffTdr61eXEAQByHIR3LBBvVdUKOFzd4hdF7dfPyhwUiLo6pUTVq6hoTnYdsr1/Hhhyh12KrZyyMInWbfqa1750yxKCAEmeQGd0Yce463RNVsEk2uCTbnDkBLBWKhI7Qr71gSbS3FxcdrSQSQSSetfLAhCiqwWwYoK9T5oUMPS+qDC/vKcVDFBTwTzlCVYmd+NmhrY0kH1+nD0lCVofbKQgp3rWPiPv/I7HCYRA0z69elBwslLidI+dIfd8/37QzyQT8LWWuwOu5x//vmMHTvWG8+ePZu33C5UgiB4ZLUIbtumQmJ+8Yv0zVn3s+NAUEvlnLkCZBeqyOq6vA48UHwb1WG1E2vrJnZciVm8oD2rV68mWFUOGIBJ9+596NShgJhlpETJL4KffQZ33tmwuutu3GG/CBqG+k2OppPQg7B8ObGo02IR1DSNmTNnpmoaogouxDM1NxaENkzWiODcuXMxTdN7zZ07l3g8lT1hWenucDSqSmqF9FS6hTXuGD457Arqug9g2TIlSKYJnTol79MMojXKHV6y6AvKNpRRTBQooCC/hAEDDiAvaGFrjYjgq6+q8jX1c4h34w5bRkrhdD11jV0bhZUrKVy7rMUiCHDIIYdw6aWXeuPly5fziL9YhCAI2SOCAJZleS81TglGMrkCUCL4ySeqRGDIZwkaBWFq8zuiGxovv6wEJ5FIZWA4ukGsziISiTDzvhkAmISB0Qwd+iOCAZOAbuFoeuPu8Pr1DfPc4nESSXe4fvHXt99uKIJupE0ioX6TVRdt0ZqgnzvvvJMOHTp44z/84Q9s2rRpzx4mCDlIVolgffwi6OtvhG2rc7oOhSGfO5ynzEY340LT1LX5+cn7dJN4XYI77riDio2bAQMTk46lnQmFuhAwHQIBJZYZLUEXv9I5jrIENdMr6uBn8eLGLcEVK1THvIgV3CNLEFTjd38j+KqqqrQ8Y0Fo6+SECEajsGtX6rib/jtmDAweEPeUp7B3B379axg6VJ13RdAtwGJrBhvXlzN9+nRCGEAv8oze9O7dR60vGhZFReo60ySZTFydmowf1yx1rVY9kNEVzstT51zsM89GP/GEtAy+XdX6XvUTvvTSSxk5cqQ3fu6555g/f/6eP1AQcoicEMG774Y330w1WnJFcPhw0BMxGDFCJd327UufPiqrBJQAOk7KEkw48Pact7EsiyBKsXp164yum9g2BHSLkhK1aVFVhYqmfuAB1c7O7wJrWmqc3IiwkpZgffLz0+MEzZHD4Mgj0yrF6HaC0tI9/zsZhsHMmTPTjk2cONFbVhCEtkxOiCAozTFNNT7qKGUFhsOkGpD4km5d19kVQ9fV/GzJUnZujwMmIXTy8/IpKcr3xDJoWBQUKNGqqgLmz1c7whs2NKw+44pgMu7Q0syMlqC/BJh/LqGQqn4NSgR9y3p7xLhx4zjvvPO88cKFC5k1a9bePVQQcoCsF0F3jc1x0oupFhb6RLDeroKrT1u3qvf27WH06G/49LPFQBcMxlEcLqK0tJQAcWxbWY0DqhaRl6fc4ZoalAWoaWob2lXWNWtU2856Dd5tI5DREozHYcAAJd7+9N5QCBaMuQxQIrgvUn+nT59Ooa8EzU033cSOJlqMCkJbIOtF0E2Rc9f23PqB0LQI5uXBD3+o3gOBBFOnnkfMUR3nTIL8+IcnYBgGQSOB48DmzTCy4r8YBnTuZnDSScmHua5vNJpaxKuqauAOJ8jsDrvTGzs2VS8QlAgmzDBxMw/dTuzx7rCf7t27c8stt3jjrVu3cuutt+79gwUhi8l6EXR7CpeVqfd4XIXHQNK1zJBzFouptTi3CdMzzzzPp59+SoLZQDXDB59EaZFqjxnUEpSUqPs0TVls555n8IMfkNr8qKiA559XX65p6UqcVOmoZWbc4XVF0DDSA6ndNUG31mAmAd0TrrrqqrRmUI899hhLlizZNw8XhCwkS0WwAFAiWF2tdKa6Wu0Qb9+uBFHXQcNp0JS4pkZlmriHYrEojz+u1sYSxDENmxN+eCYb18TQNDiwX8JbywuFkmuQrkK5Ccs1NcoldhcZM2yM1CUC3gaMH89Q/fWv4bLLvON+ETTsfZflEQwGeeihh7yxbdvSoU5o02ShCBYB17FkSRGVlfDGG2pfwnGUcLjhLvE4SiVtO00EH3hAxd8Fg+A4Nhs3biAeV3l2CWDkyGG0L8zHsGLYmkH7gjiFhVBSoqxHTSP1JW5t/7w82LQJli1TxzUt1fwkGmX7dli8IpzREkwkkiLYpw/4Kr/UtwT3JT/+8Y85/fTTvfH777/PSy+9tE+/QxCyhSwUQbWwv2JFEdXVSvx27EiJ4OGHq6u2bAEnmrTGfOrjLtsFg/DRRx9RV1cNKHf58HHjOPDAfuhWHCMRI2bmk2fEsCxVj9DTKFcEo1H1WdNUd/SjjkqNXRGMRNi4Mbm+l8Ggy7BkCeyBCG7ZkopZbAYPPvggIV8czrXXXkt1C+4XhFwha0QwHA4zYMAAevc+kJKSDnz6aSlffqncX8eBLl2UpdYxWZV+4waHr1/7Rg0yqMzOnVv497//nRwFyc/PZ/oDD2CaGoG4qjwTNQsIEfE2X7p0SV7uikc8nhI9XVevQEBZn67i1dURyDOxdTMtq8UlHs9ckNoL4G6uCD72GDz++O6vS9K/f38mT57sjTdu3Midd97Z7PsFIVfIGhEcM2YMK1eu5M0332PSpEm0b9+OtWtVZIp/P8Jdv+tX/QVF/31FDer5oY7j8OSTj5JIxAEbCDBt2jT6DBhAIADtd67BcaAy1BnTSXgi6MX5uXE5sViqtZ2uw8iRymS0rNSaYCSCUaAULdPmRjTasIUmgNs4LmGGKdq1IbUJ0xQ1Nbu/xseNN95I7969vfH999/PN99806JnCEK2kzUi6BKLpZfKct/dPQrHScYJJnamymvVE8HFixfz+edzUT/fZtCgg7n88sshEKC4GPqs+wDHga15vbwiC6v6n5Busblb0a4Iui6waaYswVgM3nkHvVa5mT/7Wfpvse2UjtanRw847TTY3OVgCmorUpsuu6OsrHnXAfn5+dx///3eOB6Pp5XmF4S2QNaJYDSaMrJcwXMLJrif+5TsYtSOt1PtQnwiWFVVxX/+8x+gDtDQNIdLLrkcPVm+pbg4dfn6ooM8EUyY4ZQl+Pe/w1tvqRNucKI7OcNIxe4kq77atup5Uj/rw/0dmURQ02D0aFVhGiDjgmImnnqqedclOeusszjuuOO88euvv86//vWvFj1DELKZrBTBNWuU/tg2Xic5dyPYtlU1acPw6UZS1T75xOHPf/6GaDQCRAGNHj260blzd3WdaXpe7cbCgcQCBRCPE1m2CjMRUZagrsOSJTB3bkr03CA/d6zrapPiyScBqCrtm3Hzw61h4K7/ZeL8CUEOO4yG5bn2EZqm8fDDD2P4cvquuuoqIpkWMAUhB8k6EdywAb79VsX6ea5vYUoEd+xQESuuBQd4GyOPPPIZW7fuAsJAGd269aJ7964pfUn6u+EwJLQAmgbVFbXYjkZR1QZlCXbunJpMNKqqNPTrlxq7Zt3nn3uXfXv0hRlF8L331HsmS9ClY7dkfGFTluBeFkIYOnQoEydO9MarVq3igQce2KtnCkK2kDUiuGrVKi6//HKeemoWq1evZdeuqKrsEkgVT3ALKJQUxjEMX43BQIAtW7bwyiuPAbXAt5jmTs444wwMQ0/pi6Z51mBCCzB8+3t0/vYjbDQsM4R2/I/grLOU+i5cqMSnQweV/Ov65AUF6fl8xx2XMQymNtX6pEkR9G5sSgSbaBjfXG677TY6+wT+zjvvZP369Xv9XEFo7WSNCJaXl/PYY48xZ877bN26lV27HHbsUBpRW6vEzxXBPDNOQYFK4th16XWg60yaNInq6p2ABjjceOPNdOnSBdOspy9Ja7DvASY/Kk7280SjNtBOxQGqKOvURsXIkXjF/xKJZHNjPaVyhx2WsWWmv25BU+6wL7Wl8WvqC6S/4UozadeuHdOmTfPGtbW1XH/99S1+jiBkGxki1Fo7BYBGZWWA6mro1Ustv7kheoYBQT1BOKx0o6KukLdeeSWZEXE2oNO9ey+uvfYGZsygoQiGQhCJ0De4kWBeDZamLLGElhSjurqUyBx1lGp1l5+fKlFdVKQsyro6VRUhrIKki4rSf4Xfg21SBPfEEvQ3X2kBF1xwAX/84x/5+OOPAXjppZf45z//mRZULQjZwvbt25t1XRaKYB6gYmPcNcDqas+TVfsUdhzTVCI4a1YtjzzyISrdLgDonH/+Bei6ErVAoJ6GdO8OlZWEK9bj6AYVXQ7ineh4BjnJ2MC6upSCuWWpSkrg+OPVvWVlYBgqW2X0GDTUUmF9S9B9xG9/u5uewqaZnoGSifrn9lAEdV3nkUce4VBfOZva2lpq/b67IOQYzRbBY445hnZ7U+N9nzEISMUra1qqIIJpJgOm7TiBAHw26iKeeeZbdu06BfgMCNCnTz969erjCZ+7psjrr8PKlSou5Ysv0KMR7PxCLC1AtdGO8Sckv961BPv1g5NPTj2kqEgpW14e0fwSli2w+PqtjpzbX8UwFxSk/wpXBHdbJ1DTfJPMQFkZuCEtJSXq/wr7YI1QENoKzRbB9957jx49enyXc2kGBkoElXIEAsoDjUSUBRgIKM044dg4PZcazOu6jvJy10raRZcuvejTp19aVpthJDXjk0/YuRPig8J0WrKEYGUJseS2c2GeTYf2NqxapToj2Tb07p0K/DNN9ZCkBfZuxVDKDzmFNRuKsO2mRTBTtekGBAKNW4IffqiKNwAcfTT84x/Njymsh23bXHHFFWnH8vPzxR0Wcposc4dNoAOgatK76bpbt0Jpaco6HNA7TmR5gmnTLgEeS96bz3nn/ZqFCw1smzRLMB6HmsIOfPzxdtZVJ7hk1y60RKGKgbYtDCwStVH4y1/UTW6GiOvHug8xTQgGiRshL/+4slLto/gKOgMtFMHCQtXKMxN+C9E1K/dQBJ977jlvPRDgnHPO4W9/+9sePUsQsoWs2R1WBIESIIDjpHaELUu9n3IKXHcdEI/z+pw5bNjwtXfnKaecR69eqlCqP4IlGFSfP/nMpLwcCnaWeVkfug5YNqZuY1fXpabhFkx1RbCeJZjQQ3Rf9xFFuzawbZu6ZK8swTFjlKueyc31C577JXsggpWVlWmtOPPz87nvvvta/BxByDayTAR7oUJcbG8jpH37VKZaXZ0ymj5fsID35s9HiebnhMMhfvKTX3lG08qVqQiXwsJkBaqkGpWWLYHevdnRvk+aJci3q1LTcHeHAwFsGz79PMDW8jhLPktg5xUQM8JoVbsYtfgZLxSmfkHVFolg586pmmH18VuCeyGCt912G1u2bPHGU6ZMoaevOZUg5CpZKIIO4KBpDrqu9Mgtg3fssVBTU8OM++5D2UxBYAfDhh2IbYc9A07XVc8QUMt627eDGVC+tO1o0KEDX9X1Ra+tRrMthtQsIPhZyk30FCwQYNky+GpVgI/nxvn2qzgVNfnUde4LmoalB7x+yPWX1dxc5/rN2DPi9tt0zUo//p3btIqyzWfZsmVpLTkHDBjAtdde26JnCEK2kmUi6AbUaWgadOqUrgG9e8Mtt9zClk2bPBE85JDRdO7ckZoaiNbECaxbhVZV6elYSUkys8TtCmcDNTXU6EXosQi6FadLbD0J1x0eO1bFBwKYJtu2qdaY2o7tmFYU2whQ1v0wsCx2tevVQAQ3blQbum670GaJYEGB8tsziWCdz01vTkxhPRzHYdKkSWk9iGfMmCGbIUKbIctEMISqLB0iLy/BkCHpNfo++ugjZsyYgYkqld+lSx9OOOEEDEPt0MZ31tKtqAqjcoeXUpeXl8xBTijhcBzAtqnVC9ENDTOuRMaqjSmh/OEPUz0yDUP1NmnXCyuaoLB6E7Uxk3heMRXhXmiO7aUQu2W4/vxnVeglGs1cXzAjmqaswfrBn4lEujvsfkkLQmRmz57N22+/7Y1PPvlkTj311GbfLwjZTpaJYHvUmqDOOfEXOYL5jB2rQva6dbOYMGECjuNgAhbwP/8znXA4TCCg1gCtSAxTtzHCAaJRpS3hcLL6TNxnCdo2tXoBaBBIRNDDASxHV+ZbIJBWKKGqCqqKurPd7KSy6SIBtVaIjh5PNWR3LT5XfGtqmrke6FJa2tAS9FuB7pcEAmrtcOHC3T6ytrY2ze0NBoPMmDGjBZMShOwna0Rw2LBhXHjhdZhmAcFgIQewmpGb/4OuwzHHQFnZuyxfvhxQ0YQ/GDuWsWN/CChvsroa7LoYAd1GNzWiUWU4hULK+rMjcarN9mwqPACnqJi4o6rIGFYUI2hS1v8oOO88NZm6Oi8cpboa8gp0tpldsW2orDEhEMDSTIx44+Wotm5toQgWFTWsHJ0pkyMQgA8+gH/+c7flt6ZNm8a6deu88TXXXMOBBx7YgkkJQvaTNXGC7du3p7S0vWqlqYFp6uTlKaOssrKcefNS/TVKi4s5/4ILWJXUgMJCFa9XErWUJYhFNKr0wq2HEKtNsKz4CDZ3/AGj+/bBWWSh2RYaYAR1duV1TXVa8olgJKL6mmy2A1gOVNeoBGZLNxnav5bG7LGVK1v4B8iUNVLfEoT0hiXV1Q0ruSb59ttvmT59ujfu0aMHU6ZMaeGkBCH7yRpLEFS8sG0r0Tn+ePVevtHihRdW4zjDgD8AXZnwq19RVFLildwvLFTN2L5YVaBE0LGIRKCkah1hM4FtOWxclyChBdi+HT5boHKT47VqndAIGMQsI7UrHIl4O7GWBcXFELNN1Qc5onJ9bc2kyNyHhUmDwXQRjETgtdcaXudPRG6ie9zVV19NNJpy1++77z4K60d0C0IbIGssQVAVpUE1ITroIFi3Dr75ZEGyRJZiyJCHGDakBkzT24HNz0/qh+Ng6ja6k6DoqwUMWfUvarv/HN1WLqClqT+HnbBxHChfG6M7YAQNogkjFR9YV+eVhfGLYCwB1dEABHUszaQ4FOWAA9INNrcPSoupL4Lz5nnl+7nqqtSio18EG2m89O9//5tXX33VGx9zzDGcc845ezApQch+skoE3a5yhq5UpLp6O+uXfQ6o8vjhcB4nn3wykeoXyTMMTwS9aA/HIWDYGNh0WPWpWhPUYsRq1aaIK4KObeM4Ol91P47Cbf9h24AxRCNmyhKsq/MqTNu28ozjCY0dO2DbrgBdDoAJY5YAJZx/fvpv0PU9LAQdDKob3R/lrxnYvn3q824swVgsxpVXXumbj87DDz+M1qxYHUHIPbLGHa6pqWHXrlocx6autopoNMp77/2XPBKovWA46aSTKCwsJBFNqHW5bTsxDF/KmuMQMhLodoL82q0q1ZcYpqbutzW1U2HHLBwHLDPE9tKBVIw5mUiinggm1wTdQ+57VZ1JMKQpoYrFPDMwkYAHH9yLSvjNKa4K6SKYoU/IjBkz+PrrVDrh7373Ow4+6sCoWgAAIABJREFU+OA9nJQgZD9ZI4KLFi1i1ao1JBIxPls4n7feeovNW9ajJ9PoDjjgQIYPHw5AImLBxo1Ys/8Po7oyTQQdNEKJGnTHUqW3EjECRroIOtGYsvCsamzdJFwYUO5wPRHctEltuMTjgKYCuGtiAYJhPVXRYelSQIXSVFbuxR/AFcF4XPnTvkIHafhF0LfmB6rB+tSpU71xx44dueOOO/ZiUoKQ/WSNCCqUEOgkeGtBgNX8Fh0dw9A57bTTmDBBQ9chXpeASATL1jCitamCpraNhkM4oRKHTROc2joCSUtQM5UIWrWqf0m+XUXCCBEu0IlaZqqVZjyOk5fPE0/ARx/53HQD6mKGsgSDQeWHJ8NYMnmbF1/cgp/uilssBmvXNh4Q7d/cqGc1Tp48mWqfi3zXXXdRUlLSgkkIQu6RZSJoAg5homx0TkAjH4OvGTVqKMXFxQQMm+6Vy0lEEhAKYTk6hpNwK+azc6fD6YO+IhxTuWymCUVL5tE5phqWB/OUCCZqo2zdCuFEDbYRIL9AT7nDSVHblcjHcVI92IcOTVa0sTXvOZim55L6l/COPx7OOANaVJ/A7w43lRFSXJz6HI16lSLmzp3L888/750aPXo0EyZMaMEEBCE3aVUi+NlnnzFz5kyq3BIv9TBx6MxmDmELrgz06NiXgQNViay8FYs4eNmLaFsrlAjaGoaTwDRVEoUVd+heuIv+Gz5QzzOVOHWNrQWgU1cDHJtYnU085lBTmcAyguQXaEQTBv95P8y2DUrU6sjzdnmjUdANTcUc2o5yh90vSLqk/rXA7t1Vr+QW4e7uRKNNN1Lyl6v597+hQwesBQvSWmoCPPLII2m9hgWhrdKqRPCpp55i0qRJ9OrVi7vvvruBGJ7AfIagFvUtQENj5OCDCAaVr2nEI3TYvpKiZR+BbXuWYJ8+bliKQ7t2QCSKrRmEw8mK+I7avOjey2Dc4RZ1VhBTs0gkwNID5BXqOGjMX5TH/72q/mTP/r98TwTjcbCCeQSDECdAIOQTwaQl6BdBc0/25N2CqXV1GTc8PA45BM48U61JLl0KiQRLbrqJxYsXe5dccMEFHH744XswCUHIPVqVCAIYhkFlZSU333xzmhg6jkN3KnBLaelA+w4dyK+pIQ8lYoFoNeGylVjbdioRtDUMK8bA0m0M7FHDoOKNGJ1KSDg6ZZ0PIRRSRlW7hMrJDRcYGJrF2tpOBPVkLrERoFuPpMVk29jx5M6xEfREMBaD7Qf+gBVDz2Kz2UOtCeq6WiTMYAnukQiGw2phsba2aRE0DDj4YHVdclOm9K23vNNFRUVprTUFoa3T6kRQT5ZWsW07TQxvueUW4oRRIriVvGCI4sJCcBx6VS0DwKjaiWbq1EU0yrYm1wStGKEnH6Hr+k/J12P84932WJEEtq10pU8fKImp4oLhAgMtqVYatgrLM4J076kTNCxwHGxLBVI7uuF5pdEo5BUaRA4cju0ot9grex2NwrZt2LtSGxJ7VKVK05SrW10NX321++u//Ra3jldv22ZE8vDtt99OVzf9TxCE1h8s7Yrhe++9xxX8lOEsYyUWHUu7UGLsYmd5lHYHFLEFMOuqiASLqQ0WsG6p4VmCuuaocll2gki4PcGYhY1O4Gc/IfjmfygtraOmBnr0NlhdZlNYCHaNpkRQD3g7vzgOVhxsTYXTuJbg+vXQv78KgbHtZLquaabc4ZkzKagJQcHvOeecVI3UFpOXB+++m37s0kthxQq8ml0u69fjaBqa45AArgMW9ejBxM6d4aWX1DUjRsDgwXs4GUHIDVq9CPrpx9cUsJ1dDCIInFk4h61GAZV5QSpsC61iM9VGO3bkF1DibKfK6YJhqTARx1FFuOrCJQzN30r/4RG0USOhqJDui/5KPA4lHQ2wEnTsCCN7rWPncghFlTVlmIBt49gajg61UZ2PPlXzqqxUVuWoUSpkpn9/UpagmzMXjUIB7FXDPv+mRygEP/qR2mWZNAlmz270NgM4Hzh/wwbSUljOPhtefnkvJiQI2U+rc4ebYgCvMZiP0QCzupp8vY5D8pZzxBEaE8IvoO3axYAOqg9HfmSHZwlCKl83Em5P7/xtHDwgWYaqQwcvpE8PGDgJ5Q6P6FwOQIGmrhs/aB04DvGYsiqra1N/Ok1TUSt9+6p9Ccsi1QM0GVLjus57tSHrls8HuOIKOOww9fnppyFD7q9W7z2Nc89V1V0FoY3T6kVQ13XatWvHb37zG6LohJLBMZptk9epCKJRwv/5B72ql0NdHYVONWEzjlNV7a0JAgwvXsvRHb8kYYZxdF/ubfv2XgjeytUGn35s4dgO8R59ARhxgFrLO7jHNk5gDtEtldiage1oXmVoTVObI+5zolGU2rl1ukiJ8F6JoBf1TXpQdLt28OKLMGsWhMM4jX2JaSohfeYZeOEFdZ8gtHFanQjaSXFyxe/OO+9k/fr1nH/++UTQac9Oz7LJ71GiTDDLUj7pokWg6+iaSn3zW4KHlKymf+EWLD2ArfvygHXd05Olyw2Gdt/J/zf0M3ae/iu2B7uy87DxLF+u4v9CpkW0rAILtSkycKD3CEKheum9ppmWwrZPLcFLLmmYgqJp8Otfw+LFxBsrhhAIqObxF17YzOYmgpD7tLo1QcuyaNeuHTfeeCOXX345RcmSVaWlpazr1Iviyu0U6Hk4RUVoZeuVAsXjagOiVy/VyUjXcKxknGAiklaV3jZMZQn6YlaKi9VmxdDhOpWbd1KaV0tlQOfV7pfRx4QvXoJLd6riC7ajESfg7S6D0pMzzkhVrorFUNWlHR3dSS+f1ey+Ipn44Q+Vinbr1uglq9asYUBjGSV1dXsYnyMIuUur+hdxySWXMGTIEC688EJP/FyGDBlC2fmTafeX33LrgHnohpaKnYvF1KtTJ9i0idqCzhTaSrAMJ8EXX6hnJIyQKrfgtwRRwjR8OGztrFFlxYk6QSIRpRdu0YN4QiNkqnvq7KDX8B1ShVvdJm+xGBAO85/XHTpXqd7priW4VyJYUAAnndToacdxeHfiRPqiNkPiQABSzeF1XW2gTJ68F5MQhNyiVbnDo0aNYuLEiQ0E0CVe0gkrEKazsY2OgUr1j1vX1T9w1/rp0gVHN3Asm7qCjoSMlFUUD+SjW/H0NUEfmga6neDzZSaff64sRH9dUvdZn27s4VWthpSwufF/sRiQl0e0NuGV9HOcFrTY3EP+8Y9/MOqbb9BR0ZTLOnTAWbAAxo1TF9h2KjxGEASglYng7nAKCnHQUlacqyrxuBJB21YlrQwdO2Gzs6gnHfJSZZ0tI4huJ7AwcOyG5Z3jcSWCjm6wZYuqVeoVXdE1TF0J57y1vTzDasAA+OUv1SX+NUE7GMZMrkeuWaOm9l2m6tbV1XHvxIkcgkopvEnXyZs7F230aHjrLZg2TU3gs89UYKMgCECWiaCdX4it6UpRSkqUJejGp7gbJMl0tZo6A8cMNhRBx6KqVueNfyZSHvH48ZCf74mgrSs/t317f0k+jdJ89az8QMyzBHv1Sm2QGIb6+mgU7FAegYS6fs0a5VZ/lyI4ffp01paV8RowFrCuvZZBQ4aok7oON9ygSvKffLJsigiCj1a1Jrg7nIIiQFPpYP364bWes6yUJWiaaIZONAYEg7QPR1ibvD9hhtFsC0cz0O0EiURSmMaO/f/bu/fgqOpDD+DfPftINiEBEgKkBIKAAQXBIvISrEXFqqBDR60i1UuvrZ0O4RFBHspLgVKKIMRi26v1tna0opb6mjKIthQw9SpBiAgJD6EkJDwSIOS9u+fcP357zp4Nu0k22c3u2fP9zGTIb7OPXzLj19/7B4wfD9d7QEa6B45GkVbp6b5xPo8HSLS5MaJXBUovp2hnCAK+brB6j3FDA+CxJ2oXtwOieuJagPAH0MmTJ7F27Vo0AJgCIDMzE0uXLr36iWPGBL6cicjEDNMSPHDgABY+9xwOVdXjq7IyVF25EjgE7XZYrBIkWdzFkWR3ITlZhNPF7gMgKR4okhUW2eN3qIGiiGswHRZfSzAjw/dz9bl2q4ymRlm0Gr1/Pf1kR1KSWB/tSUiCzdOIbl3EOGJTE2C3tHAOYAc89dRTaNAdqrBu3bqg46pE5M8wIXjlyhUcOVaMjxu64VJNDVzA1d1hfQgqHkh2cfCBogBJwwagLGsMLIoM2WKFJLv85kZKS8X5o+fKPah3iRD03qUEQByWCgA2ScbZ2hRcuRK4V6mGYFPfgVBgwTUpFwCILrJ+kiZcdu7cib/+9a9a+ZZbbsGj6iXxRNQqw4Sg4MEuiM23irotTV0io44J2myw2CRYFRecSRIsFpGNNTeMhWKRRHdYskJS/FuC2hIW2Q23IrrD+rMFGhyiZWWXPNqav2AheOECcFlJhcueBPf1N8DdNR0eD5AgucL613C5XJg9e7ZWtlgsyM/P581xRCEwXAhWQ7d/Vm0JKorvxiObDZaxYyClpiApScGVK97j91LEsfOiJSiJWWJdCKrf33iDB0mpoiXYv7/v58dvfgR4+GHYrXKLC5+dTqCsTOxM89gSIA8ajNKR9wEAEq3hDcH8/HwcPnxYKz/55JP4bshHVhOZm6EmRgAPLkKcpKJYrb4b3QCgqkrrDtuys+Dq3h3JiTL27fO+1Hs3r6SoEyMeyG4Z6v8H1AmQft9xo/81doy7X4wjzpkD7NkDlJV1AYYMgU3aC0AclxWsJahyWxPg3LcH36kXiw0dlvCFYEVFBVasWKGV09LSsGrVqrC9P5FZGK4leBJpOAjA1b27OEtPn0RuN2C3o0t3sWc3OcE3BiclJWLyZGDqvTJkyQrFIsHT4PJ7KQBY4YEjyabdV9S9O9CjB1BZKRqcanc4IUGcRPXAA/411B+Y6rE6YLEAfU7uET8LY3d40aJFftcPrFq1CuntPqiQyLwM1xIEbKgCvNs7vLPD6rYwNQTTHDgPINEhQ5J8433jxwPY50H3G6z44rg4ZAEQqeVyAemVJbDK3wKp1/t9avfu4ue1tYBt+kOoaUxAovcU++b0p11ZPU2QJMDmkIBaaEf2d1RBQQH++Mc/auUbb7wRP/vZz8Ly3kRmY7iWoNgV65WVJc7F83Z14XYDjY1wpoqWYKrTJRYvJ6T6ruCVZcAhJlTkBt+9vG43kFF5WIzz6e7mBXzB1tgIHK/shtJKJ4Jd16tvCTpctSKnk8QbhKMl6PF4MGvWLL/HeHMcUfsZLARrIVpu14iixQJMnOiboUhOxmlLX7hgx6BBwLhRLpQMnYbC7/63NuYHjwcWhwjJpFfzgffeAyBaekqCN+2aXVquv+3ygw9Ei3Do0MA11LcE7a46SBIgec/tC8eY4KuvvorCwkKtPGPGDNxyyy0dfl8iszJYCF7y/tvP95B6kgwAjBiBL77pgtffciArC3CgCee/MwKNiV19ISjL2iZfRYE4gxDeMUGHN+2a3eamD8E+fcSJ9sOGBa6hviV4qVt/X3cYgB0dC8GqqiosWbJEK3fp0gXr1q3r0HsSmZ3BQvDy1Q/pQ7CpCYpkhcvunaKtrYW6cUJb7vL11+IYLvjO+AO829oc3i5lvW+7G+Afgg6H2CscbA5Cv2zm66E/Am66CSnJMhSLhK5JHQvBZcuWoVJ3OOKyZcuQ2cLZgkTUOoNNjIhLj84gBQ3qf/zqgmlAHKRqdcBjdYi1KpcvIzFRTGAMGQKxjObMGUiKWBqj3zHicgE279H9ePBBv0/1OzbfKzk5cA3V544cCXTpYodk7Yr0bh587047pP7tD8EDBw7g5Zdf1sqDBw/GnDlz2v1+RCQYrCWoAPgb/gc3oXyy93BRdcE0RJCpy1LQtStw8SKU2jrf1RzeFLNYxa+ttQQVBW63d8wuPR0YNMjvU9VjC9UDVgcMAIJd3du7t1g2c++9wKRJgMUuZq4l77/t+q0VBbm5udrVAwCwadMmOPR3jhBRuximJThmzBhUVlaipETCtm0JGDHCW3XdSaVutwhBux3iRNTCQtzwf4W49ORCAM7gIehyweVyIBEuv3tBVBaL6BJf8g5JTpzY8mlUfuOFNu8p1k5nu0PwL3/5C3bv3q2V77//ftx1113tei8i8meYlqDdbkdaWhp69eoGp9OJ7du9YaW2BJOTtZag3Q5tIE+WgdSzR8VzvddfquN2Wgg2NIhtx3AHDEF436662vd9m1mt0M7sakcI1tTUYP78+bp6JGDDhg0hvw8RBWaYlqBK7QEeOeJ9wGYDxo4FTp6ExyNC0GqFFmayDCTXnBfP9U54qK04rXfZ0ACPJxU2xRX0IqKUFDGkCATNycBsuharK/QxwdWrV+PMmTNa+emnn8aAAQNCfh8iCswwLUGVui5ao96lm5wsQlCyi5DThWBC/SURQN6WoMUCnMq+1dcS3LIFsssjQjBIwulDMKShOHURs3rkVwhKSkrwwgsvaOV+/fph0aJFIb0HEbXMcCGYnCzG5LR7w9WWlt3uf3eS3Q5FEcNxqaeKgNWr/e7WOH3tJJT+17NaWW50waoE7w6re4m9b912+pZgCCGoKArmzp0Ll671uGHDBiTpT2ggog4zTHf47Nmz+Pvf/w4AOHYsA7J8K4AUv5DxuACrLEKjUbGjuEiM+2m7OHQhKEnAkWM29KsQM7qK2yNea+sS8PP1BzWH1BJU66dePvLvfwOjR7d69+aHH36o/b4AcPvtt+OHP/xhCB9MRG1hmBA8evQoZs6c6S2NwMyZ78PtToFN3xJshLbcpeS4Teu+aiGoLoJOTITDBpw4IcYWe/cGPC5ZtASDjAk6nb7vQ9qmq+8Ol5SIr5QUse/O4wHOnhVbUHQaGhowd+5crWyz2bB582YelkoUAYbrDgtibK++Hr6Q6doVF266C6VZY+F2Azanr8+q38+LQYOAp59GVpYollx7L2QZqK32wApP0BDUv0dIWaR2X/WTIuqq63/+E/j976/aofLCCy/gxIkTWjk3NxfXX+9/sg0RhYdBQ1Ds7W1ogK9babGgavA4yFa72P2hC0G/nmdCAiBJ6OfdflyXnIF9+4DLF70twSDNPL8gDUVGhhhEvKzb8qeODapN1cZGccEJgNOnT2PNmjXaU3v16oXly5e388OJqDWGDsGKCvgdka82tmQZaJSDzF54F/n16CGKikVCbS1gkVtuCYa0NlBPksSRX7o9v9ixA1ixwhe4+/aJnSr792P+/Pmo885iA8DatWvRVZsFIqJwM2gIiu7ju+8C//iH71F9j7OmUYSgejG6xjuroS61kS0iiCyKDEmOQEsQAAYOBM6f9y1MVFuC6snQb78NuFw4tW4dtm7dqr1s7NixeOyxxzrwwUTUGoOGoO+oq7NnfY/q7wKuabTDar1qzsGvJWi1ipYgIO4escitjwm2q0U4aJAIwMvNTsH59lsxff3++wAA+7vvaj9Sb46TWplFJqKOMeh/Yb71dvplc/X1vvV8l5qSAq9C8bYEJQmYOhVQJCtSU0VLUNveFoAafgMHtqO6vXqJiuruBNFUVADl5QCA77hcGOF9+IknnsCoUaPa8WFEFArDLJEJJikJwMSJqEvLwtH3xQ66ggLgojsFWYHyTJeakiRagjabGBMU08qB/yQ2G/D449BmlUNisYg1NhUVvg3IquJiKBYLLIoCN4D5AD5NSsL6m28G3npLPGfECP9LkIkobAwbgtnZdVAUb/f39ttRc070LAcPFiFYVuXEiEAtQd1BgLIsxgStVnHpuiIrQUMQAK65pgMVdjqBwkLgP/8J+hQrgBkAZtTViZv0VA88IMYNiSjsDNodBm6//QJ69vTNMajXgmgNPYslcHe4WQiqLUGrp0nsJY7UhUWJiaKZOnLkVT+yNPvXz8MPA6+8Epk6EZFxQxAQy+/cbuDQIV8Dy+EAJkwQ35eOvA949FH/F+lCMClJjAk6nboQbKEl2CGJieK98/KA++4DrFYowVZdq4dCvPYa8MYbuo3SRBRuhu0OAyIrXC7/nmJCgu+Ag+pBI4FBiv+LdGOCgwcD02dI6PG/gPVihFuCXbqIYPve94CjR4FJk6Dk5cGiKFc/124XF0ANHhyZuhCRxjAhmJ2d7beTIjs7G99+e/XBLA6HL8dsNvjvcXv0Ub/TDywWoP9AKxqkTmoJTpkiZlYWLUL1118j1e/YG536+sjVg4j8GOa/tL59+2Lx4sV+j6ndYZXVKr7UscCrGnXXXnv1G0tiTHDgiY+ROjDQi8JEPUrryhUgJQW75s7FvRDjES4AdkAEn9stfoF33wWefjoydSEijaHHBNXusErtBqs51qY8s1phswHjxnmXv0SqBWazAcePA+npOPb22+izdy8sEFdHfel0oqmgwDeYKcu+5TFEFFGGD0F9S1CdIfbrDgPAddeJK+IC8XaXExK837Z7k3ArbDZg927A5cKhuXMxEoAHwEIAVW+9BcfYscAnnwBr14pfoLDQ7/xDIooMQ4eg3e4LPsB3cZIagtoSmR/9CAi2B7f5DG2HNgm3QJLEgaoARp45g48AjAdweMoU3Dt1qu85CxcCe/cC99wT4pldRNQehhkTDMTh0K4NASAmYIEODutFKgRPn9aOzuoLYCqAww4HDm3cePVzx4wBPvooMvUgIj+GCcG9e/di0qRJWvnTTz9FQsItqKnxPWfKFPGv7ojB0IVjTPDIEeDAAf/H3nsPMkTTW90eZ7vnHgzat08cpQVwexxRFBgmBBVFQZOu76soChITfV3gRx/1Tf5GaoK3zZ59VszuNqNmsro9Dn/7m/hScXscUacz9Jigvueqb8Dpr/Vok9mzw1YnAMCrr4pxyGa4PY4o9hg6BPUTuYFCsM3S0oD+/UO8S7MFXbsCb74J/OEPQGIi5GBnAnJ7HFHUGaY7HEiwlmC7ziF9/PEO18ePxQLMnImmUaPgGTECzkDP4fY4oqgzdEswbN1h9ckRWJLy2p/+BGeg/cEAt8cRxYC4DsFoKysrQ+mmTVDvgtLWdesvZA8wgUJEncfQIai/EF0/Phgra4wXLFiAaS4XJIjtceeGDBHLYbg9jihmGDoE9fMY+lah2vuMZhj+61//wp4339S2x72UlYXeRUXiUFVujyOKGYYOQX3IxdKlbG63G7m5uZABbXvc6HfegaTvBnN7HFFMiMtR+Wi3BH/3u9/h4MGDAIApAGbOnIkxY8Zc/URujyOKuhhqP4VP797i0Jhx4zr/sy9cuIClS5dq5dTUVPzyl7/s/IoQUZsYpiWYkJCA7OxsvzIgrgxpPhtsswU/NCbSnnnmGVy8eFErr1y5Er169YpOZYioVRZFCbaIrdkTLRb06dMHpaWlka5TSNxu0e2NhWUx+/btw8033wz1T3r99dfjq6++gj1cO1GIKOwM0xIMJlbWGsuyjNzcXOj/n5Kfn88AJIpxcTkmGA1//vOfUVBQoJUfeOABv6O/iCg2Gb47HAuqq6uRk5ODs2fPAgCcTieOHDmCfv36RblmRNQatgTDYOXKlVoAAsCSJUsYgEQGYZiW4PHjx7Fp0yatPGfOHAwcODAqddE7fPgwhg8fDrf3xqcBAwbg0KFDSIzUMf1EFFYxMq3QuvLycuTn52vlhx56KOohqCgKZs+erQUgAGzcuJEBSGQg7A53wLZt27Bz506t/IMf/ABT1ZvjiMgQGILtVFdXh7y8PK1st9uxadMmWLgPmMhQGILttG7dOpw6dUorz5s3Dzk5OVGsERG1B0OwHU6ePIlf/epXWjkzMxPPPvtsFGtERO3FEGyHvLw8NDQ0aOVf//rXSElJiWKNiKi9GIIh2rFjB7Zt26aVJ0yYgOnTp0exRkTUEQzBEDQ1NWG27o5iSZKQn5/PyRAiA2MIhmDz5s0oLi7Wyj//+c9x4403RrFGRNRRDME2Ki8vx8qVK7Vyeno6nn/++SjWiIjCgSHYRgsXLkRNTY1WXr16NdLS0qJYIyIKB4ZgG3z22Wd4/fXXtfLIkSPxxBNPRLFGRBQuhtk7PHToUGzfvt2v3Bk8Hg9mzZrl91h+fj6ssXCUNRF1mGFCsHv37rjrrrs6/XNfeeUV7N+/Xyv/+Mc/xvjx4zu9HkQUGYY5SisaqqqqkJOTg8rKSgBASkoKiouLkZmZGeWaEVG4cEywBUuXLtUCEACWLVvGACSKM2wJBvHVV1/hpptugizLAIDBgwfj4MGDcDgcUa4ZEYWTYcYEa2trcfLkSa3cv39/JCcnR+SzFEVBbm6uFoCAWCjNACSKP4bpDu/fvx/Dhg3TvvSTFeH2xhtvYM+ePVp52rRpmDx5csQ+j4iixzAh2FmuXLmCBQsWaOXExERs2LAhijUiokhiCDazatUqlJeXa+WFCxeif//+0asQEUUUQ1CnuLgYGzdu1MrZ2dlYuHBhFGtERJHGEPRSFAVz586Fy+XSHtuwYQOcTmcUa0VEkcYQ9Prggw/8tuXdcccdmDZtWhRrRESdgSEIoKGhAfPmzdPKNpsNmzdv5mGpRCbAEASwfv16nDhxQivPnj0b1113XRRrRESdxfQheOrUKaxZs0Yr9+rVC8uXL49ijYioM5k+BOfPn4/6+nqtvG7dOqSmpkaxRkTUmUwdgp988gneeecdrTxu3DjMmDEjijUios5m2hB0uVx+N8dZLBa89NJLkCTT/kmITMkwByikp6f7LVlJT0/v0Pv95je/wTfffKOVf/rTn2LkyJEdek8iMh5THqV19uxZ5OTkoLq6GoA4tbqkpAQ9evSIcs2IqLOZsu+3ePFiLQAB4Pnnn2cAEpmU6VqCn3/+OcaOHauVhw8fjn379sFmM8zIABGFkalagrIsIzc31++x/Px8BiCRiZkqBF977TV88cUXWvmRRx7BrbfeGsUaEVG0GaY7fPDgQfziF7/Qylu2bMHw4cPb/PqLFy8iJycHFy5cAAAkJyejuLgYffr0CXtdicg4DNMPrK6uxt69e/3KoVi+fLkWgIC4SY4BSERI6/w0AAAG3ElEQVSm6A4XFRVhy5YtWvnaa6/F3Llzo1gjIooVcR+C6s1xHo9He2zTpk1ISEiIYq2IKFbEfQhu3boVu3bt0spTp07F3XffHcUaEVEsiesQrK2txfz587VyQkKC3x0iRERxHYJr1qzxm82eP38+Bg4cGMUaEVGsidsQPHbsGNavX6+V+/bti8WLF0exRkQUi+I2BOfNm4empiatvH79eiQnJ0exRkQUi+IyBD/66CN8+OGHWvn73/8+HnzwwSjWiIhiVdyFYGNjo98aQKvVivz8fN4cR0QBxV0IbtiwAceOHdPKs2bNwtChQ6NYIyKKZXEVgqWlpVi1apVWzsjIwIoVK6JXISKKeYbZOzxmzBicO3dOK3fr1u2q5yxYsAB1dXVaee3atQGfR0SkMswpMq3ZtWsXbrvtNq08evRoFBQU8OIkImpRXCSE2+0OeFgqA5CIWhMXKfHb3/4WRUVFWvknP/kJRo8eHcUaEZFRGL47fP78eeTk5ODSpUsAgK5du6KkpAQ9e/aMcs2IyAgMMzFy7tw57NixQytPnjwZPXv2xDPPPKMFIAA899xzDEAiajPDtAT37NmDiRMnauXdu3cjMTERo0ePhvorDBs2DPv37+fFSUTUZoYdE5RlGbNmzYI+w3lzHBGFyrAhuH37dnz++eda+aGHHvJbIkNE1BaGDcGXX35Z+z4pKcnv2CwiorYybAjqJ0OWLFmCvn37RrE2RGRUhg1B1YABA/DUU09FuxpEZFCGD8EXX3wRiYmJ0a4GERmUoUPw7rvvxpQpU6JdDSIysJDXkxw6dAgFBQWRqEuLjh496le2Wq148cUXeVgqEXVISIulJUlCSkoKLl++HOl6tcrpdCItLS3oz3v37o0vv/yyE2tkHKNGjUJFRUW0q0EUVnl5ecjLywv5dSG1BGVZjokABID6+nqUlZVFuxqGVFFRwb8dxZ3q6up2va7NIdinTx8AQE1NTUwEYUZGBhwOx1WPl5eXQ5blKNTIeCRJQmZmZrSrQRQWqamp7Xpdm0NQ3TNcV1eH2tradn1YR5SWlmLr1q1wOp2QJAmPPfYY+vXrd9XzsrKy2Mppo8zMzJg7FYios7V5TNAo1BBs3sox0xhha2N+ams5Fo9GI+pscXvagCzLfi3CsrIyZGVl+T0nXoORY35EbRd3Idi7d2+/cvMgNJPWxvya/62IzCjuusPNBeoaqt3BeOgyt/T7sbtL1Lq4awk2FyjU1HHD5l1mI2LXl6hjDBOCn332Ge68806t/PHHH2P8+PHteq/m3cB4WFYTqOvL7i5R6wwTgrIs+12s3pHQat46jJVlNS3N6qpd9ebPKS8vB8DlLkTtZZgQjDWhbD0LNtbY/D1aCmJ1djsWwpoonjAEA2hLiywcY3EtvYe6Q6el2W31OWq9iCh0DEGd8vLykFtbLS1DaT7WGKwr29IsdbBANuJMNlEsYgjqBJotVltbwSZPWhqLUwO1tXBt6T0YdESRxRBE8K6kvrXVPNDUVlxbtBSuLX0+EUUeQxChtbZCWVvYlnAlouhiCLZRS4EWDIOOKPYxBNuIgUYUnwx90RIRUUcxBInI1AzTHXY4HH4zqoGO1iciClXcH6VFRNQSdoeJyNQYgkRkagxBIjI1hiARmZphZodPnDiBl156SSvPmjULAwYMiGKNiCgeGGZ2eM+ePZg4caJW3r17NyZMmBDFGhFRPGB3mIhMjSFIRKbGECQiU2MIEpGpMQSJyNQYgkRkagxBIjI1hiARmRpDkIhMjSFIRKbGECQiUzPM3uGqqioUFBRo5XHjxiEtLS2KNSKieGCYECQiigR2h4nI1BiCRGRqDEEiMjXDnCxdV1eH06dPa+W+ffsiKSkpijUionhgmJZgYWEhhgwZon0VFhZGu0pEFAcME4JERJHAECQiU2MIEpGpMQSJyNQYgkRkagxBIjI1hiARmRpDkIhMjSFIRKbGECQiU2MIEpGpGeYAhbS0NEydOtWvTETUUTxZmohMjd1hIjI1hiARmRpDkIhMjSFIRKZmmBAsKirCbbfdpn0VFRVFu0pEFAcMs0Tm8uXL2LVrl1+ZiKijDNMSJCKKBIYgEZkaQ5CITI0hSESmxhAkIlNjCBKRqTEEicjUGIJEZGoMQSIyNYYgEZkaQ5CITM0wJ0s3NTWhsrJSK6enp8PhcESxRkQUDwwTgkREkcDuMBGZGkOQiEyNIUhEpmaYQ1XPnz+PnTt3auU77rgDGRkZUawREcUDw4RgcXExpk+frpV3797NECSiDuPsMBGZGscEicjUGIJEZGoMQSIyNYYgEZkaQ5CITI0hSESmxhAkIlNjCBKRqTEEicjU/h8s1Z06PmEJ1wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hddm.plotting.plot_from_data(\n",
    "    df=data,\n",
    "    generative_model=model,\n",
    "    save=False,\n",
    "    make_transparent=False,\n",
    "    path=\"tmp_figures\",\n",
    "    value_range=np.arange(-0.1, 7, 0.1),\n",
    "    plot_func=hddm.plotting._plot_func_model,\n",
    "    keep_frame=False,\n",
    "    figsize=(14, 6),\n",
    "    **{\n",
    "        \"hist_bottom\": data.a.values[0],\n",
    "        \"ylim\": 3,\n",
    "        \"add_trajectories\": True,\n",
    "        \"n_trajectories\": 10,\n",
    "        \"markersize_trajectory_rt_choice\": 100,\n",
    "        \"markertype_trajectory_rt_choice\": \"*\",\n",
    "        \"color_trajectories\": {-1.0: \"red\", 1.0: \"blue\"},\n",
    "        \"markercolor_trajectory_rt_choice\": {-1.0: \"red\", 1.0: \"blue\"},\n",
    "        \"add_data_model_markersize_starting_point\": 40,\n",
    "        \"add_data_model_markertype_starting_point\": \">\",\n",
    "        \"add_data_model_markershift_starting_point\": -0.1,\n",
    "        \"linewidth_histogram\": 2,\n",
    "        \"linewidth_model\": 2,\n",
    "        \"bin_size\": 0.1,\n",
    "        \"data_color\": \"black\",\n",
    "    }\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: More complex data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = \"angle\"\n",
    "n_samples = 500\n",
    "\n",
    "data, full_parameter_dict = simulator_h_c(\n",
    "    n_subjects=9,\n",
    "    n_trials_per_subject=n_samples,\n",
    "    model=model,\n",
    "    p_outlier=0.00,\n",
    "    conditions=None,\n",
    "    depends_on=None,\n",
    "    regression_models=None,\n",
    "    regression_covariates=None,\n",
    "    group_only_regressors=False,\n",
    "    group_only=None,\n",
    "    fixed_at_default=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 27 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hddm.plotting.plot_from_data(\n",
    "    df=data,\n",
    "    generative_model=model,\n",
    "    save=False,\n",
    "    columns=3,\n",
    "    make_transparent=False,\n",
    "    path=\"tmp_figures\",\n",
    "    value_range=np.arange(-0.1, 7, 0.1),\n",
    "    plot_func=hddm.plotting._plot_func_model,\n",
    "    keep_frame=False,\n",
    "    figsize=(12, 6),\n",
    "    **{\n",
    "        \"hist_bottom\": data.a.values[0],\n",
    "        \"ylim\": 4,\n",
    "        \"add_trajectories\": True,\n",
    "        \"n_trajectories\": 10,\n",
    "        \"markersize_trajectory_rt_choice\": 100,\n",
    "        \"markertype_trajectory_rt_choice\": \"*\",\n",
    "        \"color_trajectories\": {-1.0: \"red\", 1.0: \"blue\"},\n",
    "        \"markercolor_trajectory_rt_choice\": {-1.0: \"red\", 1.0: \"blue\"},\n",
    "        \"add_data_model_markersize_starting_point\": 40,\n",
    "        \"add_data_model_markertype_starting_point\": \">\",\n",
    "        \"add_data_model_markershift_starting_point\": -0.1,\n",
    "        \"linewidth_histogram\": 2,\n",
    "        \"linewidth_model\": 2,\n",
    "        \"bin_size\": 0.1,\n",
    "        \"data_color\": \"black\",\n",
    "    }\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4: Making gifs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the model plot, we can create fairly exciting gifs to illustrate the workings of various Sequential Sampling Models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import imageio  # package for gifs\n",
    "from hddm.simulators.basic_simulator import simulator\n",
    "from hddm.simulators import hddm_preprocess\n",
    "\n",
    "\n",
    "#\n",
    "theta = np.array([[0.1, 0.6, 0.5, 0.5]])\n",
    "theta_secondary = np.array([-0.3, 1.0, 0.5, 1.0])\n",
    "model = \"ddm\"\n",
    "model_secondary = \"ddm\"\n",
    "n_samples = 20000\n",
    "n_samples_secondary = 500  # If we want to have another reference dataset (can be useful for illustrating e.g. likelihood)\n",
    "\n",
    "create_frames = True\n",
    "make_gif = True\n",
    "add_secondary_data = False\n",
    "\n",
    "# Create trajectories (have to presimulate, then use for frame generation)\n",
    "n_trajectories = 1\n",
    "trajectories = []\n",
    "trajectory_choices = []\n",
    "for i in range(n_trajectories):\n",
    "    out_tmp = hddm.simulators.basic_simulator.simulator(\n",
    "        theta=theta, model=model, n_samples=1, delta_t=0.001\n",
    "    )\n",
    "    trajectories.append(out_tmp[2][\"trajectory\"].T)\n",
    "    trajectory_choices.append(out_tmp[1].flatten())\n",
    "\n",
    "trajectories_to_supply = np.concatenate(trajectories)\n",
    "trajectory_choices_to_supply = np.concatenate(trajectory_choices)\n",
    "\n",
    "trajectory_supply_dict = {\n",
    "    \"trajectories\": trajectories_to_supply,\n",
    "    \"trajectory_choices\": trajectory_choices_to_supply,\n",
    "}\n",
    "\n",
    "# Create data\n",
    "out = simulator(theta=theta, model=model, n_samples=n_samples, delta_t=0.001)\n",
    "\n",
    "data = hddm_preprocess(out, subj_id=\"0\", add_model_parameters=True)\n",
    "\n",
    "out_secondary = simulator(\n",
    "    theta=theta_secondary,\n",
    "    model=model_secondary,\n",
    "    n_samples=n_samples_secondary,\n",
    "    delta_t=0.001,\n",
    ")\n",
    "\n",
    "data_secondary = hddm_preprocess(out_secondary, subj_id=\"0\", add_model_parameters=True)\n",
    "\n",
    "\n",
    "if create_frames:\n",
    "    frames = []\n",
    "    cnt = 0\n",
    "    for i in range(n_trajectories):\n",
    "        # Subset precomputed trajectories for current plot\n",
    "        tmp_trajectories = {}\n",
    "        tmp_trajectories[\"trajectories\"] = trajectory_supply_dict[\"trajectories\"][\n",
    "            : (i + 1), :\n",
    "        ]\n",
    "        tmp_trajectories[\"trajectory_choices\"] = trajectory_supply_dict[\n",
    "            \"trajectory_choices\"\n",
    "        ][: (i + 1)]\n",
    "        tmp_maxid = np.argmax(np.where(tmp_trajectories[\"trajectories\"][i, :] > -999))\n",
    "\n",
    "        for j in range(10, tmp_maxid + 110, 10):\n",
    "            # Define all the plot options via a kwarg dict\n",
    "            plot_options_dict = {\n",
    "                \"alpha\": 1.0,\n",
    "                \"ylim\": 3.0,\n",
    "                \"hist_bottom\": data.a.values[0],\n",
    "                \"add_data_rts\": True,\n",
    "                \"add_data_model\": True,\n",
    "                \"add_trajectories\": True,\n",
    "                \"alpha_trajectories\": 0.1,\n",
    "                \"n_trajectories\": None,\n",
    "                \"supplied_trajectory\": tmp_trajectories,  # out[2]['trajectory'][:, 0][np.arange(0, out[2]['trajectory'][:, 0].shape[0], 10)],\n",
    "                \"maxid_supplied_trajectory\": j,\n",
    "                \"color_trajectories\": {-1.0: \"red\", 1.0: \"blue\"},\n",
    "                \"data_color\": \"black\",\n",
    "                \"bin_size\": 0.1,\n",
    "                \"linewidth_histogram\": 2,\n",
    "                \"linewidth_model\": 2,\n",
    "                \"markersize_trajectory_rt_choice\": 100,\n",
    "                \"markertype_trajectory_rt_choice\": \"*\",\n",
    "                \"markercolor_trajectory_rt_choice\": {-1.0: \"red\", 1.0: \"blue\"},\n",
    "                \"highlight_trajectory_rt_choice\": True,\n",
    "                \"add_data_model_keep_boundary\": True,\n",
    "                \"add_data_model_keep_slope\": True,\n",
    "                \"add_data_model_keep_ndt\": True,\n",
    "                \"add_data_model_keep_starting_point\": True,\n",
    "                \"add_data_model_markersize_starting_point\": 40,\n",
    "                \"add_data_model_markertype_starting_point\": \">\",\n",
    "                \"add_data_model_markershift_starting_point\": -0.05,\n",
    "                \"secondary_data\": None,  # data_secondary,\n",
    "                \"secondary_data_color\": \"blue\",\n",
    "                \"secondary_data_label\": None,\n",
    "            }\n",
    "\n",
    "            # Create plot\n",
    "            hddm.plotting.plot_from_data(\n",
    "                df=data,\n",
    "                generative_model=model,\n",
    "                save=True,\n",
    "                make_transparent=False,\n",
    "                path=\"tmp_figures\",\n",
    "                save_name=\"myfig_\" + str(cnt),\n",
    "                columns=1,\n",
    "                groupby=[\"subj_idx\"],\n",
    "                figsize=(6, 4),\n",
    "                value_range=np.arange(-0.2, 5, 0.1),\n",
    "                delta_t_model=0.001,\n",
    "                keep_frame=False,\n",
    "                keep_title=False,\n",
    "                plot_func=hddm.plotting._plot_func_model,\n",
    "                **plot_options_dict\n",
    "            )\n",
    "\n",
    "            plt.close(\"all\")\n",
    "\n",
    "            # Read image, append to frames and delete it from disk\n",
    "            image = imageio.v2.imread(\"./tmp_figures/myfig_\" + str(cnt) + \".png\")\n",
    "            frames.append(image)\n",
    "            os.remove(\"./tmp_figures/myfig_\" + str(cnt) + \".png\")\n",
    "\n",
    "            cnt += 1\n",
    "    # Append a few frames with the last image to make the gif a bit better to view\n",
    "    for j in range(100):\n",
    "        frames.append(image)\n",
    "\n",
    "imageio.mimsave(\"./tmp_gifs/example.gif\", frames, fps=50)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![SegmentLocal](./tmp_gifs/example_model_plot_tutorial.gif)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### END"
   ]
  }
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